How Does AI Search Change Customers’ Behavior?

For the majority of the last two decades, the path to discovering new e-commerce brands or products has run through Google search results. Ranking on page one of SERPs has been the primary growth lever for most online stores.

Of course, Google still holds the title of the main traffic source in e-commerce, and it won’t change in the foreseeable future. However, customers currently have a lot more options than just “googling it”.

According to an August 2025 survey from Omnisend, nearly 60% of Americans use Gen AI tools as their online shopping assistants at least occasionally. OpenAI’s Economic Research team estimates that around 2% of all ChatGPT queries involve shopping — users are asking for recommendations and comparisons, or even relying completely on LLMs’ ability to find the product they need at the best price. That leads many data-driven brands to use ChatGPT scraper API to monitor how their products are positioned within these conversational AI ecosystems.

As a result, traffic on the biggest e-commerce platforms is increasingly driven by AI. Data from Similarweb showed that, halfway through 2025, referral clicks from ChatGPT accounted for about 5% of total site visits for Walmart, Etsy, Target, and eBay. It’s still nowhere near the amount of traffic Google generates, but the number probably won’t stop growing. That’s why you can’t afford to ignore it.

And How Do AI Models Actually Decide What to Recommend?

If you want your store to show up in AI-generated answers, you have to understand the way large language models analyze your site.

Google’s algorithm is built around links and content. It crawls pages, evaluates authority through backlinks, matches keywords, and ranks results accordingly. It’s also a system that store owners have had twenty years to figure out.

AI models work differently. When a user asks ChatGPT or Perplexity for a product recommendation, the model doesn’t crawl the web in real time to simply return the highest-ranking page. Instead, it draws on its training data combination, the indexed content it has access to, and its live web-browsing capabilities. What it’s looking for isn’t exactly the most authoritative domain, in Google terms, but the most useful, specific, and trustworthy answer to the user’s prompt.

For a query like “find me the best waterproof hiking boot under $150,” these five signals matter most:

  • Completeness of product attributes — the more attributes you provide, the better the match against a user’s query.
  • Price and availability accuracy — stale pricing or incorrect stock information are among the strongest negative signals an AI model can register.
  • Conversational product descriptions — AI models reason over use-case context, not just technical specifications.
  • Customer reviews and ratings — third-party reviews on platforms like Reddit or independent review sites may carry more weight than on-site reviews alone.
  • Store website trustworthiness — consistency of data across your store’s own pages and external sources increases the model’s confidence in the brand.

What Does “AI-ready” Actually Mean for Your Store?

There are two basic questions every e-commerce brand owner has to answer:

  • How can you increase the odds of your store showing up in AI-generated recommendations?
  • How can you make users who discovered your brand through LLMs stay on your site and make a purchase?

Of course, every online store is different. Yet there are a few areas that almost always deserve priority.

AI models usually refer to sites they can understand — and, in this context, understanding means access to clean, consistent, structured information.

When a user asks an AI assistant for “a lightweight merino wool sweater for travel, under $200,” the model needs to match that query against available product data with high precision. If your product is made of merino wool, but that attribute isn’t explicitly stated — or is buried in a marketing copy paragraph — the chances of it surfacing in that recommendation drop significantly.

The same applies to price, dimensions, use cases, compatibility, and any other attribute relevant to how your customers actually shop.

Then there’s visual search.

Google Lens alone is now used for over 20 billion visual searches every month. Shoppers photograph products they spot in the real world, screenshot items they see on social media, or snap pictures in physical stores, then use those images to find them online. From a user’s point of view, that may be the single most convenient and natural way to explore new products.

Many e-commerce brands are underprepared for what visual search systems actually require: variety and context.

A single front-facing shot on a white background may be enough for a traditional product listing. It is not enough for an algorithm trying to match a user’s photo of a jacket they spotted on the street to an item in your catalog. Lifestyle shots showing the product in use from multiple angles, images that capture texture, scale, and detail — these are the kinds of assets visual search algorithms can actually work with.

For e-commerce brands, some of the most interesting developments in visual search are happening with dedicated reverse image search engines like lenso.ai. Built on advanced AI and Content-Based Image Retrieval technology, lenso.ai lets users upload a single picture and instantly find places, similar items, related visuals, and exact duplicates of that image across the web.

For online shoppers, that translates into a fast way to identify a product they’ve spotted somewhere and track down where to buy it. For store owners, the use case is just as compelling: lenso.ai helps protect product photography, trace original sources, and monitor where visual assets appear online.

In an era where a single product image can be lifted, edited, and reposted across dozens of sites within hours, that kind of visibility is no longer a nice-to-have. It is a core part of running an AI-ready e-commerce brand.

Beyond product data and visuals, Smartbees' team strongly suggests focusing on:

  • Google Shopping feed quality — Merchant Center feed completeness, accuracy, and freshness can directly influence whether products appear in AI-generated recommendations, not just Shopping ads.
  • Structured data — Schema.org microdata, especially Product, Offer, and Review, helps AI models read and interpret product pages.
  • Mobile UX — AI-driven traffic often arrives with high intent and low patience, especially on mobile devices. Invoicing and payment flow — once AI search drives new buyers to your store, a clean billing process matters. Zintego handles invoicing, estimates, and receipts in minutes.
  • Performance and scalability — new product discovery channels may bring more traffic than expected, so the site and e-commerce platform must be ready.

Optimizing your store for AI search involves a lot of work. Some of it overlaps with traditional SEO - the same fundamentals like link building, technical SEO, and content quality still help your domain earn the authority that both Google and AI models rely on. However, building a brand that is ready for AI commerce requires a technical partner that designs and develops stores with long-term adaptability in mind.

We are still in the middle of an AI-commerce evolution, and nobody knows exactly what its rules will look like in a year or two.

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